Conventional telematics deals with mostly vehicle data and measure key performance indicators (KPIs) for driver monitoring based on the vehicle data. The measurement of KPIs based on inertial sensors, by itself or in conjunction with data obtained from onboard diagnostic (OBD) sensors, results in metrics that are inward-looking. However, a broader context in which such metrics are measured is not captured. Consider a case where a driver brakes suddenly. Data from the inertial sensors and the OBD sensors provide sufficient information to determine such events. However, the data from these sensors alone is insufficient to infer the reason for such an action. For example, it is impossible to determine whether the braking was a consequence of following a car very closely (tailgating) or if it was because an animal jumped in front of the car.
Further, in addition to basic location information, existing location technologies also provides traffic information. However, computation of time required to reach the destination overlooks factors that are dynamic in nature, for example nature of vehicular traffic (predominantly heavy trucks or cars), speed breakers, traffic lights, and the like. Up-to-date information on speed limits and the like are not always available. There are some applications that use inertial sensor data to infer road conditions and provide that information on maps. However, they do not provide detailed information such as type of vehicles that ply on the road, how orderly the traffic is and the like.
Therefore, there is a need for a method and a system for driver monitoring by fusing contextual data with event data to determine context as a cause of event.